Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations41815
Missing cells615
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory128.0 B

Variable types

Text6
Categorical4
Numeric6

Alerts

Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Electric Range and 1 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Make is highly overall correlated with Electric Vehicle TypeHigh correlation
State is highly overall correlated with ZIP CodeHigh correlation
ZIP Code is highly overall correlated with StateHigh correlation
State is highly imbalanced (99.3%)Imbalance
Electric Utility has 494 (1.2%) missing valuesMissing
ZIP Code is highly skewed (γ1 = -25.0907059)Skewed
Electric Range has 10618 (25.4%) zerosZeros
Base MSRP has 39924 (95.5%) zerosZeros

Reproduction

Analysis started2024-07-22 13:35:37.791424
Analysis finished2024-07-22 13:35:52.852020
Duration15.06 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct5221
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
2024-07-22T13:35:53.163517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters418150
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1514 ?
Unique (%)3.6%

Sample

1st row1N4AZ0CP3E
2nd row1N4AZ1CP2J
3rd rowWBY1Z8C50H
4th row5YJ3E1EA0J
5th rowWVWPR7AU6K
ValueCountFrequency (%)
5yjygdee8m 223
 
0.5%
5yjygdee9m 214
 
0.5%
5yjygdeexm 201
 
0.5%
5yjygdee7m 193
 
0.5%
5yjygdee2m 191
 
0.5%
5yjygdee0m 178
 
0.4%
5yjygdee3m 172
 
0.4%
5yj3e1ebxj 168
 
0.4%
5yjygdee6m 163
 
0.4%
5yjygdee5m 161
 
0.4%
Other values (5211) 39951
95.5%
2024-07-22T13:35:53.901245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36566
 
8.7%
E 35539
 
8.5%
J 27079
 
6.5%
5 24631
 
5.9%
Y 24092
 
5.8%
A 21092
 
5.0%
3 18283
 
4.4%
C 17144
 
4.1%
D 14675
 
3.5%
G 14270
 
3.4%
Other values (23) 184779
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 36566
 
8.7%
E 35539
 
8.5%
J 27079
 
6.5%
5 24631
 
5.9%
Y 24092
 
5.8%
A 21092
 
5.0%
3 18283
 
4.4%
C 17144
 
4.1%
D 14675
 
3.5%
G 14270
 
3.4%
Other values (23) 184779
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 36566
 
8.7%
E 35539
 
8.5%
J 27079
 
6.5%
5 24631
 
5.9%
Y 24092
 
5.8%
A 21092
 
5.0%
3 18283
 
4.4%
C 17144
 
4.1%
D 14675
 
3.5%
G 14270
 
3.4%
Other values (23) 184779
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 36566
 
8.7%
E 35539
 
8.5%
J 27079
 
6.5%
5 24631
 
5.9%
Y 24092
 
5.8%
A 21092
 
5.0%
3 18283
 
4.4%
C 17144
 
4.1%
D 14675
 
3.5%
G 14270
 
3.4%
Other values (23) 184779
44.2%

County
Text

Distinct107
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size326.8 KiB
2024-07-22T13:35:54.261637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4578959
Min length4

Characters and Unicode

Total characters228211
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.1%

Sample

1st rowKing
2nd rowKing
3rd rowKing
4th rowRiverside
5th rowKing
ValueCountFrequency (%)
king 21815
51.4%
snohomish 4489
 
10.6%
pierce 3118
 
7.3%
clark 2472
 
5.8%
thurston 1579
 
3.7%
kitsap 1496
 
3.5%
whatcom 1126
 
2.7%
spokane 1019
 
2.4%
island 503
 
1.2%
skagit 490
 
1.2%
Other values (108) 4330
 
10.2%
2024-07-22T13:35:54.911600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 32974
14.4%
n 32398
14.2%
K 23526
10.3%
g 22482
9.9%
o 14295
 
6.3%
h 12020
 
5.3%
a 10989
 
4.8%
s 9270
 
4.1%
e 8976
 
3.9%
r 8303
 
3.6%
Other values (39) 52978
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 32974
14.4%
n 32398
14.2%
K 23526
10.3%
g 22482
9.9%
o 14295
 
6.3%
h 12020
 
5.3%
a 10989
 
4.8%
s 9270
 
4.1%
e 8976
 
3.9%
r 8303
 
3.6%
Other values (39) 52978
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 32974
14.4%
n 32398
14.2%
K 23526
10.3%
g 22482
9.9%
o 14295
 
6.3%
h 12020
 
5.3%
a 10989
 
4.8%
s 9270
 
4.1%
e 8976
 
3.9%
r 8303
 
3.6%
Other values (39) 52978
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 32974
14.4%
n 32398
14.2%
K 23526
10.3%
g 22482
9.9%
o 14295
 
6.3%
h 12020
 
5.3%
a 10989
 
4.8%
s 9270
 
4.1%
e 8976
 
3.9%
r 8303
 
3.6%
Other values (39) 52978
23.2%

City
Text

Distinct475
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
2024-07-22T13:35:55.387521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length19
Mean length8.2404161
Min length3

Characters and Unicode

Total characters344573
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)0.3%

Sample

1st rowRENTON
2nd rowREDMOND
3rd rowSEATTLE
4th rowWILDOMAR
5th rowAUBURN
ValueCountFrequency (%)
seattle 7842
 
16.2%
bellevue 2192
 
4.5%
redmond 1516
 
3.1%
vancouver 1447
 
3.0%
island 1338
 
2.8%
kirkland 1333
 
2.7%
sammamish 1220
 
2.5%
bothell 1138
 
2.3%
olympia 1014
 
2.1%
renton 997
 
2.1%
Other values (507) 28443
58.7%
2024-07-22T13:35:56.794837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 49684
14.4%
A 34818
 
10.1%
L 33168
 
9.6%
T 26468
 
7.7%
N 23031
 
6.7%
S 21508
 
6.2%
O 21403
 
6.2%
R 17704
 
5.1%
I 15304
 
4.4%
M 13631
 
4.0%
Other values (17) 87854
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 49684
14.4%
A 34818
 
10.1%
L 33168
 
9.6%
T 26468
 
7.7%
N 23031
 
6.7%
S 21508
 
6.2%
O 21403
 
6.2%
R 17704
 
5.1%
I 15304
 
4.4%
M 13631
 
4.0%
Other values (17) 87854
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 49684
14.4%
A 34818
 
10.1%
L 33168
 
9.6%
T 26468
 
7.7%
N 23031
 
6.7%
S 21508
 
6.2%
O 21403
 
6.2%
R 17704
 
5.1%
I 15304
 
4.4%
M 13631
 
4.0%
Other values (17) 87854
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 49684
14.4%
A 34818
 
10.1%
L 33168
 
9.6%
T 26468
 
7.7%
N 23031
 
6.7%
S 21508
 
6.2%
O 21403
 
6.2%
R 17704
 
5.1%
I 15304
 
4.4%
M 13631
 
4.0%
Other values (17) 87854
25.5%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
WA
41701 
CA
 
25
MD
 
17
VA
 
10
TX
 
5
Other values (32)
 
57

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters83630
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowCA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 41701
99.7%
CA 25
 
0.1%
MD 17
 
< 0.1%
VA 10
 
< 0.1%
TX 5
 
< 0.1%
CT 4
 
< 0.1%
NC 4
 
< 0.1%
FL 4
 
< 0.1%
NJ 3
 
< 0.1%
PA 3
 
< 0.1%
Other values (27) 39
 
0.1%

Length

2024-07-22T13:35:57.280721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 41701
99.7%
ca 25
 
0.1%
md 17
 
< 0.1%
va 10
 
< 0.1%
tx 5
 
< 0.1%
ct 4
 
< 0.1%
nc 4
 
< 0.1%
fl 4
 
< 0.1%
nj 3
 
< 0.1%
pa 3
 
< 0.1%
Other values (27) 39
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 41747
49.9%
W 41702
49.9%
C 37
 
< 0.1%
M 24
 
< 0.1%
D 22
 
< 0.1%
N 18
 
< 0.1%
T 13
 
< 0.1%
V 12
 
< 0.1%
L 7
 
< 0.1%
P 6
 
< 0.1%
Other values (13) 42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 41747
49.9%
W 41702
49.9%
C 37
 
< 0.1%
M 24
 
< 0.1%
D 22
 
< 0.1%
N 18
 
< 0.1%
T 13
 
< 0.1%
V 12
 
< 0.1%
L 7
 
< 0.1%
P 6
 
< 0.1%
Other values (13) 42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 41747
49.9%
W 41702
49.9%
C 37
 
< 0.1%
M 24
 
< 0.1%
D 22
 
< 0.1%
N 18
 
< 0.1%
T 13
 
< 0.1%
V 12
 
< 0.1%
L 7
 
< 0.1%
P 6
 
< 0.1%
Other values (13) 42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 41747
49.9%
W 41702
49.9%
C 37
 
< 0.1%
M 24
 
< 0.1%
D 22
 
< 0.1%
N 18
 
< 0.1%
T 13
 
< 0.1%
V 12
 
< 0.1%
L 7
 
< 0.1%
P 6
 
< 0.1%
Other values (13) 42
 
0.1%

ZIP Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct595
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98131.074
Minimum745
Maximum99567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:35:57.634713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum745
5-th percentile98006
Q198052
median98121
Q398370
95-th percentile98926
Maximum99567
Range98822
Interquartile range (IQR)318

Descriptive statistics

Standard deviation3024.2316
Coefficient of variation (CV)0.030818287
Kurtosis655.63479
Mean98131.074
Median Absolute Deviation (MAD)100
Skewness-25.090706
Sum4.1033508 × 109
Variance9145976.5
MonotonicityNot monotonic
2024-07-22T13:35:58.089451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 1070
 
2.6%
98004 773
 
1.8%
98033 763
 
1.8%
98115 739
 
1.8%
98006 660
 
1.6%
98012 643
 
1.5%
98074 640
 
1.5%
98040 608
 
1.5%
98103 587
 
1.4%
98034 570
 
1.4%
Other values (585) 34762
83.1%
ValueCountFrequency (%)
745 1
< 0.1%
2124 1
< 0.1%
2842 1
< 0.1%
6340 2
< 0.1%
6371 1
< 0.1%
6379 1
< 0.1%
7094 1
< 0.1%
7438 1
< 0.1%
8033 1
< 0.1%
11201 1
< 0.1%
ValueCountFrequency (%)
99567 1
 
< 0.1%
99403 19
 
< 0.1%
99402 6
 
< 0.1%
99362 96
0.2%
99361 4
 
< 0.1%
99360 2
 
< 0.1%
99357 3
 
< 0.1%
99356 1
 
< 0.1%
99354 68
0.2%
99353 48
0.1%

Model Year
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.3373
Minimum1998
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:35:58.553316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2013
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7817191
Coefficient of variation (CV)0.0013782231
Kurtosis-0.04230082
Mean2018.3373
Median Absolute Deviation (MAD)2
Skewness-0.68291154
Sum84396775
Variance7.7379613
MonotonicityNot monotonic
2024-07-22T13:35:59.022016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2021 8164
19.5%
2018 6073
14.5%
2019 4650
11.1%
2020 4624
11.1%
2017 4206
10.1%
2022 4194
10.0%
2016 2772
 
6.6%
2015 2207
 
5.3%
2013 2181
 
5.2%
2014 1595
 
3.8%
Other values (8) 1149
 
2.7%
ValueCountFrequency (%)
1998 1
 
< 0.1%
1999 1
 
< 0.1%
2000 6
 
< 0.1%
2002 2
 
< 0.1%
2008 5
 
< 0.1%
2010 4
 
< 0.1%
2011 364
 
0.9%
2012 766
 
1.8%
2013 2181
5.2%
2014 1595
3.8%
ValueCountFrequency (%)
2022 4194
10.0%
2021 8164
19.5%
2020 4624
11.1%
2019 4650
11.1%
2018 6073
14.5%
2017 4206
10.1%
2016 2772
 
6.6%
2015 2207
 
5.3%
2014 1595
 
3.8%
2013 2181
 
5.2%

Make
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
TESLA
17634 
NISSAN
5837 
CHEVROLET
4316 
FORD
2372 
KIA
2046 
Other values (28)
9610 

Length

Max length20
Median length14
Mean length5.6078441
Min length3

Characters and Unicode

Total characters234492
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNISSAN
2nd rowNISSAN
3rd rowBMW
4th rowTESLA
5th rowVOLKSWAGEN

Common Values

ValueCountFrequency (%)
TESLA 17634
42.2%
NISSAN 5837
 
14.0%
CHEVROLET 4316
 
10.3%
FORD 2372
 
5.7%
KIA 2046
 
4.9%
TOYOTA 1741
 
4.2%
BMW 1717
 
4.1%
VOLKSWAGEN 963
 
2.3%
AUDI 862
 
2.1%
VOLVO 677
 
1.6%
Other values (23) 3650
 
8.7%

Length

2024-07-22T13:35:59.362008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 17634
42.1%
nissan 5837
 
14.0%
chevrolet 4316
 
10.3%
ford 2372
 
5.7%
kia 2046
 
4.9%
toyota 1741
 
4.2%
bmw 1717
 
4.1%
volkswagen 963
 
2.3%
audi 862
 
2.1%
volvo 677
 
1.6%
Other values (28) 3676
 
8.8%

Most occurring characters

ValueCountFrequency (%)
S 32152
13.7%
A 30909
13.2%
E 29742
12.7%
T 26273
11.2%
L 24553
10.5%
N 14107
 
6.0%
O 13337
 
5.7%
I 11023
 
4.7%
R 8759
 
3.7%
V 6662
 
2.8%
Other values (17) 36975
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 32152
13.7%
A 30909
13.2%
E 29742
12.7%
T 26273
11.2%
L 24553
10.5%
N 14107
 
6.0%
O 13337
 
5.7%
I 11023
 
4.7%
R 8759
 
3.7%
V 6662
 
2.8%
Other values (17) 36975
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 32152
13.7%
A 30909
13.2%
E 29742
12.7%
T 26273
11.2%
L 24553
10.5%
N 14107
 
6.0%
O 13337
 
5.7%
I 11023
 
4.7%
R 8759
 
3.7%
V 6662
 
2.8%
Other values (17) 36975
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 32152
13.7%
A 30909
13.2%
E 29742
12.7%
T 26273
11.2%
L 24553
10.5%
N 14107
 
6.0%
O 13337
 
5.7%
I 11023
 
4.7%
R 8759
 
3.7%
V 6662
 
2.8%
Other values (17) 36975
15.8%

Model
Text

Distinct106
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
2024-07-22T13:35:59.786720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length21
Mean length6.2040894
Min length2

Characters and Unicode

Total characters259424
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowLEAF
2nd rowLEAF
3rd rowI3
4th rowMODEL 3
5th rowE-GOLF
ValueCountFrequency (%)
model 17622
27.2%
3 8319
12.8%
leaf 5837
 
9.0%
y 5062
 
7.8%
s 2918
 
4.5%
volt 2182
 
3.4%
ev 2117
 
3.3%
bolt 2013
 
3.1%
niro 1502
 
2.3%
prius 1438
 
2.2%
Other values (99) 15730
24.3%
2024-07-22T13:36:00.486485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 30741
11.8%
E 30638
11.8%
O 26964
 
10.4%
22925
 
8.8%
M 20128
 
7.8%
D 18911
 
7.3%
A 11699
 
4.5%
I 9908
 
3.8%
3 9882
 
3.8%
F 8416
 
3.2%
Other values (27) 69212
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 30741
11.8%
E 30638
11.8%
O 26964
 
10.4%
22925
 
8.8%
M 20128
 
7.8%
D 18911
 
7.3%
A 11699
 
4.5%
I 9908
 
3.8%
3 9882
 
3.8%
F 8416
 
3.2%
Other values (27) 69212
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 30741
11.8%
E 30638
11.8%
O 26964
 
10.4%
22925
 
8.8%
M 20128
 
7.8%
D 18911
 
7.3%
A 11699
 
4.5%
I 9908
 
3.8%
3 9882
 
3.8%
F 8416
 
3.2%
Other values (27) 69212
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 30741
11.8%
E 30638
11.8%
O 26964
 
10.4%
22925
 
8.8%
M 20128
 
7.8%
D 18911
 
7.3%
A 11699
 
4.5%
I 9908
 
3.8%
3 9882
 
3.8%
F 8416
 
3.2%
Other values (27) 69212
26.7%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
Battery Electric Vehicle (BEV)
31031 
Plug-in Hybrid Electric Vehicle (PHEV)
10784 

Length

Max length38
Median length30
Mean length32.063183
Min length30

Characters and Unicode

Total characters1340722
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowPlug-in Hybrid Electric Vehicle (PHEV)
4th rowBattery Electric Vehicle (BEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 31031
74.2%
Plug-in Hybrid Electric Vehicle (PHEV) 10784
 
25.8%

Length

2024-07-22T13:36:00.815403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-22T13:36:01.112283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
electric 41815
23.5%
vehicle 41815
23.5%
battery 31031
17.4%
bev 31031
17.4%
plug-in 10784
 
6.1%
hybrid 10784
 
6.1%
phev 10784
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e 156476
11.7%
136229
10.2%
c 125445
9.4%
i 105198
 
7.8%
t 103877
 
7.7%
l 94414
 
7.0%
V 83630
 
6.2%
r 83630
 
6.2%
E 83630
 
6.2%
B 62062
 
4.6%
Other values (13) 306131
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1340722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 156476
11.7%
136229
10.2%
c 125445
9.4%
i 105198
 
7.8%
t 103877
 
7.7%
l 94414
 
7.0%
V 83630
 
6.2%
r 83630
 
6.2%
E 83630
 
6.2%
B 62062
 
4.6%
Other values (13) 306131
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1340722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 156476
11.7%
136229
10.2%
c 125445
9.4%
i 105198
 
7.8%
t 103877
 
7.7%
l 94414
 
7.0%
V 83630
 
6.2%
r 83630
 
6.2%
E 83630
 
6.2%
B 62062
 
4.6%
Other values (13) 306131
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1340722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 156476
11.7%
136229
10.2%
c 125445
9.4%
i 105198
 
7.8%
t 103877
 
7.7%
l 94414
 
7.0%
V 83630
 
6.2%
r 83630
 
6.2%
E 83630
 
6.2%
B 62062
 
4.6%
Other values (13) 306131
22.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
Clean Alternative Fuel Vehicle Eligible
24916 
Eligibility unknown as battery range has not been researched
10618 
Not eligible due to low battery range
6280 
Eligibility unknown as battery
 
1

Length

Max length60
Median length39
Mean length44.031902
Min length30

Characters and Unicode

Total characters1841194
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Clean Alternative Fuel Vehicle Eligible 24916
59.6%
Eligibility unknown as battery range has not been researched 10618
25.4%
Not eligible due to low battery range 6280
 
15.0%
Eligibility unknown as battery 1
 
< 0.1%

Length

2024-07-22T13:36:01.339081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-22T13:36:01.618309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eligible 31196
11.8%
clean 24916
9.4%
alternative 24916
9.4%
fuel 24916
9.4%
vehicle 24916
9.4%
battery 16899
 
6.4%
range 16898
 
6.4%
not 16898
 
6.4%
unknown 10619
 
4.0%
as 10619
 
4.0%
Other values (7) 61313
23.2%

Most occurring characters

ValueCountFrequency (%)
e 280139
15.2%
222291
12.1%
l 189574
10.3%
i 154700
 
8.4%
n 119823
 
6.5%
t 117427
 
6.4%
a 115484
 
6.3%
r 79949
 
4.3%
b 69332
 
3.8%
g 58713
 
3.2%
Other values (16) 433762
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1841194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 280139
15.2%
222291
12.1%
l 189574
10.3%
i 154700
 
8.4%
n 119823
 
6.5%
t 117427
 
6.4%
a 115484
 
6.3%
r 79949
 
4.3%
b 69332
 
3.8%
g 58713
 
3.2%
Other values (16) 433762
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1841194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 280139
15.2%
222291
12.1%
l 189574
10.3%
i 154700
 
8.4%
n 119823
 
6.5%
t 117427
 
6.4%
a 115484
 
6.3%
r 79949
 
4.3%
b 69332
 
3.8%
g 58713
 
3.2%
Other values (16) 433762
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1841194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 280139
15.2%
222291
12.1%
l 189574
10.3%
i 154700
 
8.4%
n 119823
 
6.5%
t 117427
 
6.4%
a 115484
 
6.3%
r 79949
 
4.3%
b 69332
 
3.8%
g 58713
 
3.2%
Other values (16) 433762
23.6%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean101.25604
Minimum0
Maximum337
Zeros10618
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:36:01.908696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median68
Q3215
95-th percentile291
Maximum337
Range337
Interquartile range (IQR)215

Descriptive statistics

Standard deviation103.07649
Coefficient of variation (CV)1.0179787
Kurtosis-1.1493703
Mean101.25604
Median Absolute Deviation (MAD)68
Skewness0.60754225
Sum4233920
Variance10624.763
MonotonicityNot monotonic
2024-07-22T13:36:02.218332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10618
25.4%
215 2645
 
6.3%
84 1941
 
4.6%
220 1702
 
4.1%
238 1589
 
3.8%
25 1217
 
2.9%
53 1062
 
2.5%
208 1041
 
2.5%
19 1034
 
2.5%
291 999
 
2.4%
Other values (86) 17966
43.0%
ValueCountFrequency (%)
0 10618
25.4%
6 419
 
1.0%
8 18
 
< 0.1%
9 15
 
< 0.1%
10 75
 
0.2%
11 1
 
< 0.1%
12 54
 
0.1%
13 194
 
0.5%
14 462
 
1.1%
15 28
 
0.1%
ValueCountFrequency (%)
337 29
 
0.1%
330 136
 
0.3%
322 701
1.7%
308 225
 
0.5%
293 161
 
0.4%
291 999
2.4%
289 233
 
0.6%
270 86
 
0.2%
266 548
1.3%
265 62
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2383.5447
Minimum0
Maximum845000
Zeros39924
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:36:02.529209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12274.895
Coefficient of variation (CV)5.1498488
Kurtosis555.60828
Mean2383.5447
Median Absolute Deviation (MAD)0
Skewness12.210596
Sum99665540
Variance1.5067305 × 108
MonotonicityNot monotonic
2024-07-22T13:36:02.792685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 39924
95.5%
69900 648
 
1.5%
34600 216
 
0.5%
31950 175
 
0.4%
52900 118
 
0.3%
28500 104
 
0.2%
38500 73
 
0.2%
54950 71
 
0.2%
32250 67
 
0.2%
59900 62
 
0.1%
Other values (26) 356
 
0.9%
ValueCountFrequency (%)
0 39924
95.5%
28500 104
 
0.2%
31950 175
 
0.4%
32250 67
 
0.2%
32995 2
 
< 0.1%
33950 36
 
0.1%
34600 216
 
0.5%
34995 9
 
< 0.1%
35390 2
 
< 0.1%
36800 19
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 5
< 0.1%
110950 4
 
< 0.1%
109000 3
 
< 0.1%
102000 10
< 0.1%
98950 5
< 0.1%
91250 2
 
< 0.1%
90700 4
 
< 0.1%
89100 2
 
< 0.1%
81100 5
< 0.1%

Legislative District
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing114
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean29.990672
Minimum0
Maximum49
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:36:03.074060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median34
Q343
95-th percentile48
Maximum49
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.596062
Coefficient of variation (CV)0.48668673
Kurtosis-0.95088419
Mean29.990672
Median Absolute Deviation (MAD)11
Skewness-0.55642751
Sum1250641
Variance213.04502
MonotonicityNot monotonic
2024-07-22T13:36:03.371886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 2756
 
6.6%
45 2700
 
6.5%
48 2386
 
5.7%
36 2056
 
4.9%
46 1856
 
4.4%
43 1823
 
4.4%
5 1664
 
4.0%
1 1655
 
4.0%
34 1327
 
3.2%
37 1313
 
3.1%
Other values (40) 22165
53.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1655
4.0%
2 458
 
1.1%
3 220
 
0.5%
4 304
 
0.7%
5 1664
4.0%
6 367
 
0.9%
7 198
 
0.5%
8 397
 
0.9%
9 226
 
0.5%
ValueCountFrequency (%)
49 591
 
1.4%
48 2386
5.7%
47 626
 
1.5%
46 1856
4.4%
45 2700
6.5%
44 941
 
2.3%
43 1823
4.4%
42 642
 
1.5%
41 2756
6.6%
40 1042
 
2.5%

DOL Vehicle ID
Real number (ℝ)

Distinct41814
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.9706316 × 108
Minimum4777
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.8 KiB
2024-07-22T13:36:03.666368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4777
5-th percentile9901988.8
Q11.3956917 × 108
median1.7917363 × 108
Q32.216996 × 108
95-th percentile4.7514774 × 108
Maximum4.7925477 × 108
Range4.7925 × 108
Interquartile range (IQR)82130433

Descriptive statistics

Standard deviation1.0358884 × 108
Coefficient of variation (CV)0.52566313
Kurtosis1.6011799
Mean1.9706316 × 108
Median Absolute Deviation (MAD)40457666
Skewness1.1347158
Sum8.2399989 × 1012
Variance1.0730647 × 1016
MonotonicityNot monotonic
2024-07-22T13:36:03.993879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250845815 1
 
< 0.1%
106918892 1
 
< 0.1%
200413349 1
 
< 0.1%
194129127 1
 
< 0.1%
171548079 1
 
< 0.1%
176430803 1
 
< 0.1%
117100077 1
 
< 0.1%
117100518 1
 
< 0.1%
169591530 1
 
< 0.1%
349980753 1
 
< 0.1%
Other values (41804) 41804
> 99.9%
ValueCountFrequency (%)
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
62261 1
< 0.1%
63486 1
< 0.1%
96589 1
< 0.1%
111667 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478909938 1
< 0.1%
478904569 1
< 0.1%
478887076 1
< 0.1%
478879985 1
< 0.1%
478875407 1
< 0.1%
Distinct591
Distinct (%)1.4%
Missing2
Missing (%)< 0.1%
Memory size326.8 KiB
2024-07-22T13:36:04.434821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.841054
Min length21

Characters and Unicode

Total characters1205931
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)0.4%

Sample

1st rowPOINT (-122.132064 47.494834)
2nd rowPOINT (-122.024951 47.670286)
3rd rowPOINT (-122.303604 47.716244)
4th rowPOINT (-117.261693 33.614732)
5th rowPOINT (-122.148214 47.292978)
ValueCountFrequency (%)
point 41813
33.3%
47.678465 1070
 
0.9%
122.122018 1070
 
0.9%
122.132064 798
 
0.6%
122.203169 773
 
0.6%
47.619011 773
 
0.6%
47.678406 763
 
0.6%
122.188994 763
 
0.6%
122.297534 739
 
0.6%
47.685291 739
 
0.6%
Other values (1172) 76138
60.7%
2024-07-22T13:36:05.203903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 125059
 
10.4%
1 100597
 
8.3%
4 97928
 
8.1%
. 83626
 
6.9%
83626
 
6.9%
7 77888
 
6.5%
6 59179
 
4.9%
3 55133
 
4.6%
8 50165
 
4.2%
5 49698
 
4.1%
Other values (10) 423032
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1205931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 125059
 
10.4%
1 100597
 
8.3%
4 97928
 
8.1%
. 83626
 
6.9%
83626
 
6.9%
7 77888
 
6.5%
6 59179
 
4.9%
3 55133
 
4.6%
8 50165
 
4.2%
5 49698
 
4.1%
Other values (10) 423032
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1205931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 125059
 
10.4%
1 100597
 
8.3%
4 97928
 
8.1%
. 83626
 
6.9%
83626
 
6.9%
7 77888
 
6.5%
6 59179
 
4.9%
3 55133
 
4.6%
8 50165
 
4.2%
5 49698
 
4.1%
Other values (10) 423032
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1205931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 125059
 
10.4%
1 100597
 
8.3%
4 97928
 
8.1%
. 83626
 
6.9%
83626
 
6.9%
7 77888
 
6.5%
6 59179
 
4.9%
3 55133
 
4.6%
8 50165
 
4.2%
5 49698
 
4.1%
Other values (10) 423032
35.1%

Electric Utility
Text

MISSING 

Distinct64
Distinct (%)0.2%
Missing494
Missing (%)1.2%
Memory size326.8 KiB
2024-07-22T13:36:05.648800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.709325
Min length11

Characters and Unicode

Total characters1847434
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
2nd rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
3rd rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 40269
12.9%
37556
12.0%
wa 25685
 
8.2%
tacoma 25319
 
8.1%
sound 24221
 
7.7%
energy 24221
 
7.7%
puget 23942
 
7.6%
inc||city 14618
 
4.7%
power 9211
 
2.9%
city 8345
 
2.7%
Other values (97) 79919
25.5%
2024-07-22T13:36:06.611484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271985
14.7%
O 136375
 
7.4%
T 129780
 
7.0%
N 129407
 
7.0%
A 126019
 
6.8%
E 120044
 
6.5%
C 100019
 
5.4%
I 99987
 
5.4%
Y 67691
 
3.7%
U 63519
 
3.4%
Other values (26) 602608
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1847434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
271985
14.7%
O 136375
 
7.4%
T 129780
 
7.0%
N 129407
 
7.0%
A 126019
 
6.8%
E 120044
 
6.5%
C 100019
 
5.4%
I 99987
 
5.4%
Y 67691
 
3.7%
U 63519
 
3.4%
Other values (26) 602608
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1847434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
271985
14.7%
O 136375
 
7.4%
T 129780
 
7.0%
N 129407
 
7.0%
A 126019
 
6.8%
E 120044
 
6.5%
C 100019
 
5.4%
I 99987
 
5.4%
Y 67691
 
3.7%
U 63519
 
3.4%
Other values (26) 602608
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1847434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
271985
14.7%
O 136375
 
7.4%
T 129780
 
7.0%
N 129407
 
7.0%
A 126019
 
6.8%
E 120044
 
6.5%
C 100019
 
5.4%
I 99987
 
5.4%
Y 67691
 
3.7%
U 63519
 
3.4%
Other values (26) 602608
32.6%

Interactions

2024-07-22T13:35:49.592972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:40.211596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:41.965226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:44.263913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:46.288668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:48.002688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:49.881751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:40.477436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:42.367833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:44.630111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:46.550771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:48.293435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:50.124119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:40.867956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:42.748542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:45.052626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:46.807811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:48.549054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:50.388081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:41.128834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:43.116528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:45.457206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:47.082725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:48.832128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:50.638573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:41.371270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:43.479037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:45.737809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:47.333964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:49.077576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:50.902063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:41.630981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:43.866629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:46.020258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:47.729631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-22T13:35:49.337565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-22T13:36:06.879153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Base MSRPClean Alternative Fuel Vehicle (CAFV) EligibilityDOL Vehicle IDElectric RangeElectric Vehicle TypeLegislative DistrictMakeModel YearStateZIP Code
Base MSRP1.0000.0220.0270.0410.0280.0040.343-0.1920.0000.002
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.0221.0000.3360.6730.7340.0430.4920.3810.0000.000
DOL Vehicle ID0.0270.3361.000-0.0620.072-0.0100.090-0.1100.014-0.003
Electric Range0.0410.673-0.0621.0000.6190.0310.451-0.3850.006-0.034
Electric Vehicle Type0.0280.7340.0720.6191.0000.0850.7970.2190.0250.015
Legislative District0.0040.043-0.0100.0310.0851.0000.0600.0270.012-0.367
Make0.3430.4920.0900.4510.7970.0601.0000.2290.0000.011
Model Year-0.1920.381-0.110-0.3850.2190.0270.2291.0000.000-0.074
State0.0000.0000.0140.0060.0250.0120.0000.0001.0000.989
ZIP Code0.0020.000-0.003-0.0340.015-0.3670.011-0.0740.9891.000

Missing values

2024-07-22T13:35:51.302171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-22T13:35:51.960406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-22T13:35:52.552150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility
01N4AZ0CP3EKingRENTONWA980592014NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84.00.05.0250845815.0POINT (-122.132064 47.494834)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
11N4AZ1CP2JKingREDMONDWA980532018NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible151.00.045.0309178936.0POINT (-122.024951 47.670286)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
2WBY1Z8C50HKingSEATTLEWA981252017BMWI3Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible97.00.046.08751711.0POINT (-122.303604 47.716244)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
35YJ3E1EA0JRiversideWILDOMARCA925952018TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible215.00.0NaN196439911.0POINT (-117.261693 33.614732)NaN
4WVWPR7AU6KKingAUBURNWA980922019VOLKSWAGENE-GOLFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible125.00.047.0161777584.0POINT (-122.148214 47.292978)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
55YJ3E1EB5LCharlesWALDORFMD206032020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible322.00.0NaN105044733.0POINT (-76.960928 38.62935)NaN
65YJ3E1EB9KSnohomishEDMONDSWA980262019TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible220.00.021.0474859535.0POINT (-122.333046 47.829439)PUGET SOUND ENERGY INC
71G1FY6S07LYakimaMOXEEWA989362020CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible259.00.015.0125943058.0POINT (-120.248988 46.562451)NaN
85YJYGDEE6LSnohomishMILL CREEKWA980122020TESLAMODEL YBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible291.00.044.0128824202.0POINT (-122.201515 47.843376)PUGET SOUND ENERGY INC
9JTDKARFPXLStevensTUMTUMWA990342020TOYOTAPRIUS PRIMEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range25.00.07.0197490114.0POINT (-117.7489 47.884242)NaN
VIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility
418051G1FY6S00MClarkWASHOUGALWA986712021CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.018.0152332498.0POINT (-122.261993 45.622947)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)
41806JTDKARFP0HGrays HarborWESTPORTWA985952017TOYOTAPRIUS PRIMEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range25.00.019.0171287014.0POINT (-124.110109 46.883808)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF GRAYS HARBOR COUNTY
418075YJSA1H23FKingKIRKLANDWA980332015TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible208.00.048.0176119541.0POINT (-122.188994 47.678406)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
418085YJYGDEE5MKingBELLEVUEWA980072021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.048.0150398951.0POINT (-122.143231 47.611666)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
418095YJ3E1EBXNKingENUMCLAWWA980222022TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.031.0187300382.0POINT (-121.991415 47.199452)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
41810WA1F2AFY3MKingSEATTLEWA981072021AUDIQ5 EPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range18.00.036.0135055168.0POINT (-122.375438 47.667804)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
418115YJ3E1EB5NKingSEATTLEWA981012022TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.043.0193653092.0POINT (-122.334341 47.611423)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
418125YJYGDEE3MKingLAKE FOREST PARKWA981552021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0.00.046.0184908349.0POINT (-122.303324 47.756486)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
418135YJ3E1EB4LKingSEATTLEWA981092020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible322.00.036.0133581416.0POINT (-122.346385 47.630685)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
418145YJ3E1EB9MKingKIRKLANDWA980342021TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as batteryNaNNaNNaNNaNNaNNaN